{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a href=\"https://colab.research.google.com/github/uptrain-ai/uptrain/blob/main/examples/checks/custom/cosine_similarity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1 align=\"center\">\n",
        "  <a href=\"https://uptrain.ai\">\n",
        "    <img width=\"300\" src=\"https://user-images.githubusercontent.com/108270398/214240695-4f958b76-c993-4ddd-8de6-8668f4d0da84.png\" alt=\"uptrain\">\n",
        "  </a>\n",
        "</h1>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1 style=\"text-align: center;\">Evaluating Cosine Similarity</h1>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**What is Cosine Similarity?**: Cosine similarity measures the similarity between two vectors of an inner product space. It is measured by the cosine of the angle between two vectors and determines whether two vectors are pointing in roughly the same direction. It is often used to measure document similarity in text analysis.\n",
        "\n",
        "**How is it useful in evaluating Large Language Models?**: It can be used to evaluate the quality of a language model by comparing the cosine similarity between the embeddings of two sentences. If the cosine similarity is high, then the language model is able to capture the semantic similarity between the two sentences. If the cosine similarity is low, then the language model is not able to capture the semantic similarity between the two sentences. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        " If you face any difficulties, need some help with using UpTrain or want to brainstorm on custom evaluations for your use-case, [speak to the maintainers of UpTrain here](https://calendly.com/uptrain-sourabh/30min)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create an API Key\n",
        "\n",
        "To get started, you will first need to get your API key from the [Uptrain Website](https://uptrain.ai/dashboard).\n",
        "\n",
        "1. Login with Google\n",
        "2. Click on \"Create API Key\"\n",
        "3. Copy the API key and save it somewhere safe\n",
        "\n",
        "\n",
        "#### Install the Uptrain Python Package"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# !pip install uptrain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 1: Create an API Client"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "from uptrain import APIClient, Settings\n",
        "\n",
        "UPTRAIN_API_KEY = \"up-*********************\"  # Insert your UpTrain API key here\n",
        "\n",
        "settings = Settings(\n",
        "    uptrain_access_token=UPTRAIN_API_KEY,\n",
        ")\n",
        "\n",
        "client = APIClient(settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 2: Add dataset\n",
        "Unlike the previous method where you had to create a dataset in Python, this method requires you to upload a file containing your dataset. The supported file formats are:\n",
        "\n",
        "- .csv\n",
        "- .json\n",
        "- .jsonl\n",
        "\n",
        "You can add the dataset file to the UpTrain platform using the `add_dataset` method.\n",
        "\n",
        "To upload your dataset file, you will need to specify the following parameters:\n",
        "- `name`: The name of your dataset\n",
        "- `fpath`: The path to your dataset file\n",
        "\n",
        "Let's say you have a dataset file called `qna-notebook-data.jsonl` in your current directory. You can upload it using the code below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'name': 'qna-dataset', 'version': 4}"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "url = \"https://uptrain-assets.s3.ap-south-1.amazonaws.com/data/qna-streamlit-docs.jsonl\"\n",
        "dataset_path = os.path.join(os.getcwd(), \"qna-notebook-data.jsonl\")\n",
        "\n",
        "if not os.path.exists(dataset_path):\n",
        "    import httpx\n",
        "\n",
        "    r = httpx.get(url)\n",
        "    with open(dataset_path, \"wb\") as f:\n",
        "        f.write(r.content)\n",
        "\n",
        "client.add_dataset(name=\"qna-dataset\", fpath=dataset_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 3: Add checksets\n",
        "A checkset contains the operators you wish to evaluate your model on. You can learn more about checksets [here](/key-components/checkset).\n",
        "\n",
        "You can add a checkset using the `add_checkset` method.\n",
        "\n",
        "To add a checkset, you will need to specify the following parameters:\n",
        "- `name`: The name of your checkset\n",
        "- `checkset`: The checkset you wish to add\n",
        "- `settings`: The settings you defined while creating the API client"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from uptrain.framework import Check, CheckSet\n",
        "from uptrain.operators import CosineSimilarity, JsonReader, Histogram, RougeScore\n",
        "\n",
        "rouge_score = RougeScore(\n",
        "    score_type=\"precision\",\n",
        "    col_in_generated=\"response\",\n",
        "    col_in_source=\"document_text\",\n",
        "    col_out=\"hallucination-score\",\n",
        ")\n",
        "\n",
        "cosine_similarity = CosineSimilarity(\n",
        "    col_in_vector_1=\"question_embeddings\",\n",
        "    col_in_vector_2=\"context_embeddings\",\n",
        "    col_out=\"similarity-question-context\",\n",
        ")\n",
        "\n",
        "list_checks = [\n",
        "    Check(\n",
        "        name=\"hallucination_check\",\n",
        "        operators=[rouge_score],\n",
        "        plots=[\n",
        "            Histogram(props=dict(x=\"hallucination-score\", nbins=20)),\n",
        "        ],\n",
        "    ),\n",
        "    Check(\n",
        "        name=\"similarity_check\",\n",
        "        operators=[cosine_similarity],\n",
        "        plots=[\n",
        "            Histogram(\n",
        "                props=dict(x=\"similarity-question-context\", nbins=20)\n",
        "            ),\n",
        "        ],\n",
        "    ),\n",
        "]\n",
        "\n",
        "check_set = CheckSet(\n",
        "    source=JsonReader(fpath=dataset_path),\n",
        "    checks=list_checks\n",
        ")\n",
        "\n",
        "client.add_checkset(\n",
        "    name=\"qna-checkset\",\n",
        "    checkset=check_set,\n",
        "    settings=settings\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 4: Add run\n",
        "A run is a combination of a dataset and a checkset. You can learn more about runs [here](/key-components/run).\n",
        "\n",
        "You can add a run using the `add_run` method.\n",
        "\n",
        "To add a run, you will need to specify the following parameters:\n",
        "- `dataset`: The name of the dataset you wish to add\n",
        "- `checkset`: The name of the checkset you wish to add"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "response = client.add_run(\n",
        "    dataset=\"qna-dataset\",\n",
        "    checkset=\"qna-checkset\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 5: View the results\n",
        "You can view the results of your evaluation by using the `get_run` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'run_id': '3532199a8887459386f53f806f740239',\n",
              " 'created_at': '2024-01-10T11:25:45.595074',\n",
              " 'updated_at': '2024-01-10T11:25:51.640624',\n",
              " 'status': 'completed',\n",
              " 'result': '',\n",
              " 'dataset': 'qna-dataset',\n",
              " 'checkset': 'qna-checkset',\n",
              " 'dataset_version': 4,\n",
              " 'checkset_version': 3}"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.get_run(response[\"run_id\"])"
      ]
    },
    {
      "attachments": {
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        }
      },
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "You can also view the results on the [UpTrain Dashboard](https://demo.uptrain.ai/dashboard/) by entering your API key as a password.\n",
        "\n",
        "![image.png](attachment:image.png)"
      ]
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